This Project investigates the problem of detecting a distributed target within the presence of signal mismatch during a partially homogeneous environment. To design a selective detector, we modify the first hypothesis take a look at by injecting a fictitious interference underneath the null hypothesis check. The fictitious interference is assumed to be orthogonal to the nominal signal within the whitened subspace. Then, in keeping with the generalized likelihood ratio criterion, we acquire a detector that has the constant false alarm rate property and is additional selective than existing detectors. However, the proposed detector works solely in the case of huge variety of training information. To overcome this limitation, we tend to introduce a tunable detector, that is parameterized by a tunable parameter. It will work under a very loose constraint on a variety of coaching knowledge. More importantly, the directivity property (robustness and selectivity) of the tunable detector can be flexibly adjusted through the tunable parameter. In addition, the tunable detector will achieve roughly the same detection performance as the corresponding generalized likelihood ratio test when no signal mismatch happens. Numerical examples are given to demonstrate the effectiveness of the proposed detectors.

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PROJECT TITLE :Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018ABSTRACT:With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although

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